Immune Multiobjective Optimization Research Articles

Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot

In order to improve the welding efficiency of the ship welding robot, the path planning of the welding robot based on immune optimization is proposed by taking the welding path length and energy loss as the optimization goals. First, on the basis of the definition of the path planning of the welding robot, the grid modeling of the robot’s working environment and the triangular modeling of the welding weldments are carried out. Then, according to the working process of the welding robot, the length objective function, including the welded seam path and the welding torch path without welding, is constructed, and the energy loss function is constructed based on the kinematics and Lagrange function. Finally, the immune optimization algorithm based on cluster analysis and self-circulation is introduced to realize the multi-objective optimization of the path planning for the ship welding robot. The test results of four kinds of ship welding weldments show that compared with the simple genetic algorithm, immune genetic algorithm, ant colony algorithm, artificial bee colony, particle swarm optimization, and immune cloning optimization, the proposed multi-objective immune planning algorithm is the best in terms of planning path length, energy consumption, and stability. Furthermore, the average shortest path and its standard deviation, the average minimum energy consumption and its standard deviation, and the average lowest convergence generation and its standard deviation are reduced by an average of 9.03%, 54.04%, 8.23%, 19.10%, 27.84%, and 52.25%, respectively, which fully verifies the effectiveness and superiority of the proposed welding robot path planning algorithm.

Multi-Objective Immune Algorithm for Internet of Vehicles for Data Offloading

On the Internet of Vehicle (IoV) devices, offloading data is the major problem because massive amounts of data generate energy consumption, and the execution cost is high. At present, accidents traffic management is highly prominent due to increased vehicles among the population. IoV is the only technology to help the transport system effectively. This data outreach the memory also has high energy consumption, and the storage cost is high. To overcome these issues, a Mobility aware Offloading scheme with Multi-Objective Immune Optimization algorithm (MOS-MOIO) is used in the cloud storage. The data is generated from the online sensor system. The generated information must alert the IoV users with a useful data offloading scheme. The result of the data offloading is evaluated with various performance metrics.

An effective integrated control with intelligent optimization for wastewater treatment process

Wastewater treatment process (WWTPs) is an enormous and complex system, it involves multi-disciplines and multi-fields, which is a greatly challenge for industrial Information Integration Engineering (IIIE). In this paper, an integrated control framework for WWTPs is proposed using intelligent optimization to solve the problem of excessive pollutant and high energy consumption. It realizes the effective integration of data model, intelligent decision making, dynamic optimization and control. Firstly, a self-organization soft sensing method is adopted. It can predict the concentrations of ammonia nitrogen and total nitrogen in advance. And the early warning is provided for decision making. Secondly, for the complex dynamic characteristics of multi-performance indexes intercoupling in WWTPs, the dynamic multi-objective immune optimization algorithm is designed. And an optimized set point with high quality in real time is to be obtained. In addition, according to different over standard states, the intelligent decision scheme of WWTPs is designed. It can achieve the effective cooperation of over standard suppression and energy saving. Finally, BSM1 simulation model is used to verify the method. The experimental results show that the integrated framework can achieve the whole process of WWTPs real-time standards and can effectively reduce energy consumption, so it has competitive practical application prospect for IIIE.

Personalized Web Service Recommendation Method Based on Hybrid Social Network and Multi-Objective Immune Optimization

To alleviate the cold-start problem and data sparsity in web service recommendation and meet the personalized needs of users, this paper proposes a personalized web service recommendation method based on a hybrid social network and multi-objective immune optimization. The network adds the element of the service provider, which can provide more real information and help alleviate the cold-start problem. Then, according to the proposed service recommendation framework, multi-objective immune optimization is used to fuse multiple attributes and provide personalized web services for users without adjusting any weight coefficients. Experiments were conducted on real data sets, and the results show that the proposed method has high accuracy and a low recall rate, which is helpful to improving personalized recommendation.

P-MOIA-RS: a multi-objective optimization and decision-making algorithm for recommendation systems

Besides accuracy, diversity of recommendation list is also important for users. Hence, the optimization of the recommendation system can be abstracted as a multi-objective problem because accuracy and diversity are contradictory goals. Available multi-objective optimization based recommendation schemes return the Pareto set for the target users. However, the scale of Pareto solutions is uncontrollable. If a Pareto set contains too many solutions, it will not be quite useful for users to make the final decision. In this paper, multi-objective immune algorithm is used to improve recommendation accuracy and diversity, then we can get the pareto set. Further, we introduce PROMETHEE into the recommendation system to get a more precise evaluation of Pareto solutions. By combining PROMETHEE with Pareto, we redefine recommendation as Top-n PROMETHEE Pareto optimization problem and a multi-objective immune optimization and decision-making algorithm is presented. The experimental results show that the proposed algorithm, compared with other existing algorithms, can generate more diverse and accurate recommendation list and provide more precise decision-making for the user.

Fast multiobjective immune optimization approach solving multiobjective interval number programming

Abstract As an uncertain programming model with multiple conflicting performance indices and bounded uncertain parameter(s), multiobjective interval number programming is a daunting topic in the fields of mathematics and intelligent optimization. Despite its comprehensive engineering application background, it is still open, and further studies are needed on basic theory, model transformation and intelligent optimizers. Therein, this work not only gropes a new shortcut to tackling one such model, but also proposes a novel multiobjective interval number immune optimization algorithm. The intrinsic solution relation between the model and a related natural interval extension one is discovered in terms of the new concept of optimal-value vector solution, by which a fast interval nondominated sorting approach is acquired. The algorithm mainly consists of population division, proliferation, evolution, selection and memory update, in which a co-evolutionary mechanism is designed to promote the current population to move quickly towards the Pareto front with the assistance of the sorting approach and an external archive set. The algorithm's resource consumption depends mainly on the archive's size. Comparative experiments have validated that the optimizer can effectively perform well over the compared approaches and is significantly superior to them with regard to efficiency.

Recommendation System Based on Singular Value Decomposition and Multi-Objective Immune Optimization

Recommendation system plays a significant role in helping people to get effective information from mass data. Traditional recommendation systems focus on the recommendation accuracy, which is not sufficient. In this paper, we also consider the various needs of users to achieve more diverse recommendation. However, accuracy and diversity are two conflicting goals for recommendation system. Hence, we model the recommendation system as a multi-objective optimization problem, and aim to find tradeoff solutions between the two goals. Because the rating matrix is rather sparser in recommendation, we first use singular value decomposition to get the recommendation list, then multi-objective immune algorithm is used to optimize it. The experimental results illustrate that the proposed algorithm can get more diverse and accurate recommendation results.

Constrained nondominated neighbor immune multiobjective optimization algorithm for multimedia delivery

In recent years, artificial immune system (AIS) algorithms is considered to be an effective method to solve the multiobjective optimization problems (MOPs), such as multimedia delivery problem. Though a decent number of solution algorithms have been proposed for MOPs, far less progress has been made for constrained multiobjective optimization problems (CMOPs), which demands a combination of constraints handling technique and search algorithm, e.g. Nondominated Neighbor Immune Algorithm (NNIA). In this paper, we propose a hybrid constraint handling technique of adaptive penalty function and objectivization of constraint violations. In our approach, the dominant population is updated via a method of objectivization of constraint violations and proportional reduction while a modified adaptive penalty function method based on the structure of the search algorithm (NNIA) is utilized to update the active population. We combine the proposed hybrid constraint handling method with NNIA to form the proposed Constrained Nondominated Neighbor Immune Algorithm (C-NNIA) to address the constrained multiobjective optimization problems. To our knowledge, it is the first time NNIA has been applied as the search algorithm for CMOPs. Numerical simulations indicate that the proposed algorithm outperforms the current state-of-the-art algorithms, i.e. NSGA-II-WTY, in both convergence and diversity.

Design of sub-wavelength grating structure for filter regardless of polarization

For isotropic dielectric thin films, polarization effect is an inherent character. As it will make the performance of optical-electric system go to bad, such polarization-dependent properties are often intolerable and should eliminate its effect in many applications. So the design of polarization insensitive filter is an indispensability work. In this paper, based on a four-part period grating, a filter regardless of polarization is gained by combing rigorous wave theory and multiobjective immune optimization algorithm. Its working wavelength is 1315nm which is often used in laser system. The results of our design shows that TE and TM polarized wave have a reflection 0.482 and 0.485, respectively, at designed wavelength 1315nm. And it denotes that two values all approach to the design objective good, and the polarization deviation is very little for their difference is 0.003. Therefore, the effect of polarization deviation at 1315nm wavelength can be eliminated very well by the designed filter.

An immune multi-objective optimization algorithm with differential evolution inspired recombination

According to the regularity of continuous multi-objective optimization problems (MOPs), an immune multi-objective optimization algorithm with differential evolution inspired recombination (IMADE) is proposed in this paper. In the proposed IMADE, the novel recombination provides two types of candidate searching directions by taking three recombination parents which distribute along the current Pareto set (PS) within a local area. One of the searching direction provides guidance for finding new points along the current PS, and the other redirects the search away from the current PS and moves towards the target PS. Under the background of the SBX (Simulated binary crossover) recombination which performs local search combined with random search near the recombination parents, the new recombination operator utilizes the regularity of continuous MOPs and the distributions of current population, which helps IMADE maintain a more uniformly distributed PF and converge much faster. Experimental results have demonstrated that IMADE outperforms or performs similarly to NSGAII, NNIA, PESAII and OWMOSaDE in terms of solution quality on most of the ten testing MOPs. IMADE converges faster than NSGAII and OWMOSaDE. The efficiency of the proposed DE recombination and the contributions of DE and SBX recombination to IMADE have also been experimentally investigated in this work.

Study on Fuzzy Clustering of Joining with Local Learning Operator in the Artificial Immune Multi-objective Optimization Algorithm

Study on Fuzzy Clustering of Joining with Local Learning Operator in the Artificial Immune Multi-objective Optimization Algorithm

Design of polarization insensitive filters with micro- and nano-grating structures

For isotropic dielectric thin films, polarization effect is an inherent characteristic. As it will make the performance of optical-electric system go to bad, such polarization-dependent properties are often intolerable and should be eliminated in many applications. In this paper, based on a micro- and nano-optical structure whose period consists of four parts, a polarization insensitive filter is obtained by combining rigorous wave theory and multi-objective immune optimization algorithm. Its working wavelength is 1315 nm which is often used in laser systems. The results of our design show that TE and TM polarized waves have reflectivities of 0.482 and 0.485, respectively at designed wavelength of 1315 nm. And it denotes that two values are both close to the design values, their difference is only 0.003, and polarization deviation is also very little. Therefore, the designed filter can eliminate the effect of polarization deviation very well at 1315 nm wavelength.

Hybrid Immune Algorithm with EDA for Multi-Objective Optimization

PDF HTML阅读 XML下载 导出引用 引用提醒 基于免疫算法和EDA 的混合多目标优化算法 DOI: 10.3724/SP.J.1001.2013.04341 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(61303119); 国家教育部博士点基金(20090203120016, 20100203120008); 博士后面上基金(20090461283, 20090451369, 200801426, 20080431228, 201104658); 陕西省自然科学基础研究计划(2011JQ8010, 2009JQ8015); 中央高校基本科研业务费专项资金(K5051203007, K5051203002) Hybrid Immune Algorithm with EDA for Multi-Objective Optimization Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:在免疫多目标优化算法的基础上,引入了分布估计算法(EDA)对进化种群进行建模采样的思想,提出了一种求解复杂多目标优化问题的混合优化算法HIAEDA(hybrid immune algorithm with EDA for multi-objectiveoptimization).HIAEDA 的进化过程混合了两种后代产生策略:一种是基于交叉变异的克隆选择算子,用于在父代种群周围进行局部搜索的同时开辟新的搜索区域;另一种是基于EDA 的模型采样算子,用于学习多目标优化问题决策变量之间的相关性,提高算法求解复杂多目标优化问题的能力.在分析两种算子搜索行为的基础上,讨论了两者在功能上的互补性,并利用有限马尔可夫链的性质证明了HIAEDA 算法的收敛性.对测试函数和实际工程问题的仿真实验结果表明,HIAEDA 与NSGAII 算法和基于EDA 的进化多目标优化算法RM-MEDA 相比,在收敛性和多样性方面均表现出明显优势,尤其是对于决策变量之间存在非线性关联的复杂多目标优化问题,优势更为突出. Abstract:The estimation of distribution algorithm (EDA) is a new type of evolutionary computation approach which reproduces offspring individuals by modeling and sampling the probability distribution of the evolving population. In this paper, the idea of EDA is introduced into the immune multi-objective optimization algorithm to form a hybrid algorithm termed as HIAEDA (hybrid immune algorithm with EDA for multi-objective optimization). It is proposed for solving complex multi-objective optimization problems (MOPs). In HIAEDA, two types of reproducing strategies are combined. One is a recombination and mutation based immune clonal selection operator. It performs a local search around the parent population and develops new searching areas. The other is a EDA based modeling and sampling operator. It learns the variable linkages and promotes the algorithm's capability of solving complex problems. By analyzing the searching behavior of the two operators, the paper comes to the conclusion that their functions are complementary to each other. The convergence of HIAEDA is proved using the theory of the finite Markov chain. Experimental results on benchmarking and real problems show that HIAEDA outperforms the outstanding NSGAII and the EDA based RM-MEDA in terms of both convergence and diversity, especially when solving complex MOPs with nonlinear relationship between decision variables. 参考文献 相似文献 引证文献

Memetic Immune Algorithm for Multiobjective Optimization

PDF HTML阅读 XML下载 导出引用 引用提醒 求解多目标问题的Memetic免疫优化算法 DOI: 10.3724/SP.J.1001.2013.04282 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点基础研究发展计划(973)(2013CB329402); 国家自然科学基金(61072139, 61001202, 61272279); 高等学校学科创新引智计划(B07048); 教育部长江学者和创新团队发展计划(IRT1170); 国家教育部博士点新教师基金(20090203120016, 20100203120008); 中国博士后科学基金(20090461283); 陕西省自然科学基础研究计划(2011JQ8010, 2009JQ8015) Memetic Immune Algorithm for Multiobjective Optimization Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:将基于Pareto支配关系的局部下山算子和差分算子引入免疫多目标优化算法之中,提出了一种求解多目标问题的Memetic免疫优化算法(Memetic immune algorithm for multiobjective optimization,简称MIAMO).该算法利用种群中抗体在决策空间上的位置关系设计了两种有效的启发式局部搜索策略,提高了免疫多目标优化算法的求解效率.仿真实验结果表明,MIAMO与其他4种有效的多目标优化算法相比,不仅在求得Pareto最优解集的逼近性、均匀性和宽广性上有明显优势,而且算法的收敛速度与免疫多目标优化算法相比明显加快. Abstract:A Memetic immune algorithm for multiobjective optimization (MIAMO) is proposed by introducing two types of local search operators. These operators are the Pareto dominance based descent operator and the differential evolution based operator. In MIAMO, the position and spatial relations between antibodies in the decision space are used to design the two heuristic local searching strategies with the assistance of which the efficiency of the immune multiobjective optimization algorithm can be improved. Experimental results indicate that, comparing with the other four efficient multiobjective optimization algorithms, the MIAMO performs better in approximation, uniformity, and coverage. It converges significantly faster than the immune multiobjective optimization algorithm. 参考文献 相似文献 引证文献

A novel micro-population immune multiobjective optimization algorithm

In this paper, we present a novel immune multiobjective optimization algorithm based on micro-population, which adopts a novel adaptive mutation operator for local search and an efficient fine-grained selection operator for archive update. With the external archive for storing nondominated individuals, the population diversity can be well preserved using an efficient fine-grained selection procedure performed on the micro-population. The adaptive mutation operator is executed according to the fitness values, which promotes to use relatively large steps for boundary and less-crowded individuals in high probability. Therefore, the exploratory capabilities are enhanced. When comparing the proposed algorithm with a recently proposed immune multiobjective algorithm and a scatter search multiobjective algorithm in various benchmark functions, simulations show that the proposed algorithm not only improves convergence ability but also preserves population diversity adequately in most cases.

Artificial immune multi-objective SAR image segmentation with fused complementary features

Artificial immune systems (AIS) are the computational systems inspired by the principles and processes of the vertebrate immune system. AIS-based algorithms typically mimic the human immune system’s characteristics of learning and adaptability to solve some complicated problems. Here, an artificial immune multi-objective optimization framework is formulated and applied to synthetic aperture radar (SAR) image segmentation. The important innovations of the framework are listed as follows: (1) an efficient and robust immune, multi-objective optimization algorithm is proposed, which has the features of adaptive rank clones and diversity maintenance by K-nearest-neighbor list; (2) besides, two conflicting, fuzzy clustering validity indices are incorporated into this framework and optimized simultaneously and (3) moreover, an effective, fused feature set for texture representation and discrimination is constructed and researched, which utilizes both the Gabor filter’s ability to precisely extract texture features in low- and mid-frequency components and the gray level co-occurrence probability’s (GLCP) ability to measure information in high-frequency. Two experiments with synthetic texture images and SAR images are implemented to evaluate the performance of the proposed framework in comparison with other five clustering algorithms: fuzzy C-means (FCM), single-objective genetic algorithm (SOGA), self-organizing map (SOM), wavelet-domain hidden Markov models (HMTseg), and spectral clustering ensemble (SCE). Experimental results show the proposed framework has obtained the better performance in segmenting SAR images than other five algorithms and behaves insensitive to the speckle noise.

Dynamic biclustering of microarray data by multi-objective immune optimization

BackgroundNewly microarray technologies yield large-scale datasets. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. Systematic analysis of those datasets provides the increasing amount of information, which is urgently needed in the post-genomic era. Biclustering, which is a technique developed to allow simultaneous clustering of rows and columns of a dataset, might be useful to extract more accurate information from those datasets. Biclustering requires the optimization of two conflicting objectives (residue and volume), and a multi-objective artificial immune system capable of performing a multi-population search. As a heuristic search technique, artificial immune systems (AISs) can be considered a new computational paradigm inspired by the immunological system of vertebrates and designed to solve a wide range of optimization problems. During biclustering several objectives in conflict with each other have to be optimized simultaneously, so multi-objective optimization model is suitable for solving biclustering problem.ResultsBased on dynamic population, this paper proposes a novel dynamic multi-objective immune optimization biclustering (DMOIOB) algorithm to mine coherent patterns from microarray data. Experimental results on two common and public datasets of gene expression profiles show that our approach can effectively find significant localized structures related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The mined patterns present a significant biological relevance in terms of related biological processes, components and molecular functions in a species-independent manner.ConclusionsThe proposed DMOIOB algorithm is an efficient tool to analyze large microarray datasets. It achieves a good diversity and rapid convergence.

A hybrid immune multiobjective optimization algorithm

In this paper, we develop a hybrid immune multiobjective optimization algorithm (HIMO) based on clonal selection principle. In HIMO, a hybrid mutation operator is proposed with the combination of Gaussian and polynomial mutations (GP-HM operator). The GP-HM operator adopts an adaptive switching parameter to control the mutation process, which uses relative large steps in high probability for boundary individuals and less-crowded individuals. With the generation running, the probability to perform relative large steps is reduced gradually. By this means, the exploratory capabilities are enhanced by keeping a desirable balance between global search and local search, so as to accelerate the convergence speed to the true Pareto-optimal front in the global space with many local Pareto-optimal fronts. When comparing HIMO with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that HIMO performs better evidently.

Probabilistic Dominance-Based Multi-Objective Immune Optimization Algorithm in Noisy Environments

Probabilistic Dominance-Based Multi-Objective Immune Optimization Algorithm in Noisy Environments

Fuzzy multi-objective immune optimization algorithm-based conceptual design

Scheme design is the key process of conceptual design.Because scheme design is a combination optimization problem,it is difficult that how to avoid combination explore to produce some feasible schemes.The model of conceptual de- sign is built from the point of views of fuzzy multi-objective optimization.The theorem of multi-fuzzy-set algebra sum and the theorem of fuzzy set compare are presented,and then ac- cording to the relationships between structures and functions of the production,the fuzzy mathematical model of the multi-objective optimization of conceptual design is provided. In order to solve the multi-objective optimization question,an effective multi-objective optimal algorithm based on the evolu- tions of the immune cells,whose convergence and diversity are better than SPEA.An example of transmission scheme design of the belt conveyor is given to show that this method is able to produce the feasible schemes set and realize the automatic scheme design.